Synchronized Resource Scaling for Application Stacks
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Solution Overview
Problem
Current resource scaling technologies are unable to automatically scale different resource types in tandem, and determining when and how to scale is a time-consuming manual process prone to errors, especially in response to unanticipated load and traffic spikes.
Innovation Solution
A scaling service that determines correspondence between usage levels of different resource types within an application stack, sets scaling criteria based on historical data, and automatically adjusts resource capacities in response to predefined thresholds or predicted usage patterns, allowing for synchronized scaling of multiple resource types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual resource scaling is used, then resource capacity can be adjusted, but the process is time-consuming and error-prone
Solution Approach 1:
The system enables self-service automatic scaling by monitoring resource usage metrics and automatically triggering scaling actions when predefined thresholds are met, eliminating the need for manual intervention and significantly reducing the time required for resource capacity adjustments
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring resource usage metrics and using this information to automatically adjust resource capacity, creating a closed-loop control system that responds dynamically to changing workload conditions without manual input
2Adaptability or versatility
If single resource type scaling is implemented, then individual resources can be adjusted, but multiple resource types cannot be scaled in tandem
Solution Approach 1:
The system merges the scaling control of multiple resource types into a unified scaling policy that can simultaneously adjust compute, storage, and network resources in tandem, ensuring coordinated scaling across the entire application stack to maintain performance consistency
Solution Approach 2:
The scaling system is designed with multi-functionality to handle diverse resource types (compute instances, storage volumes, network bandwidth) through a single universal scaling mechanism, allowing any combination of resource types to be scaled together based on application-specific policies
3Device complexity
If fixed virtual resources are allocated, then resource management is simple, but the system cannot accommodate unanticipated load and traffic spikes
Solution Approach 1:
The system transitions from static fixed resource allocation to dynamic resource provisioning by implementing automatic scaling that continuously monitors workload conditions and adjusts resource capacity in real-time, enabling the system to adapt to unanticipated load and traffic spikes while maintaining manageable complexity through automation
Data Source
AI summary
A notification for an application stack is received, where the application stack includes a plurality of resource types. At least one policy associated with the notification is obtained, with the first policy being a policy for scaling a first resource of a first resource type and a second resource of a second resource type of the application stack. A first capacity for the first resource and a second capacity for the second resource is determined based at least in part on the at least one policy. The first resource and the second resource are caused to be scaled according to the first capacity and the second capacity respectively.